Johansson Helena, Oden Anders, Johnell Olof, Jonsson Bengt, de Laet Chris, Oglesby Alan, McCloskey Eugene V, Kayan Karthik, Jalava Tarja, Kanis John A
Statistician, Romelanda, Sweden.
J Bone Miner Res. 2004 Jun;19(6):906-13. doi: 10.1359/jbmr.2004.19.6.906.
The aim of this study was to develop a methodology to optimize the role of BMD measurements in a case finding strategy. We studied 2113 women > or = 75 years of age randomly selected from Sheffield, UK, and adjacent regions. Baseline assessment included hip BMD and clinical risk factors. Outcomes included death and fracture in women followed for 6723 person-years.
Poisson models were used to identify significant risk factors for all fractures and for death with and without BMD and the hazard functions were used to compute fracture probabilities. Women were categorized by fracture probability with and without a BMD assessment. A 10-year fracture probability threshold of 35% was taken as an intervention threshold. Discordance in categorization of risk (i.e., above or below the threshold probability) between assessment with and without BMD was examined by logistic regression as probabilities of re-classification. Age, prior fracture, use of corticosteroids, and low body mass index were identified as significant clinical risk factors.
A total of 16.8% of women were classified as high risk based on these clinical risk factors. The average BMD in these patients was approximately 1 SD lower than in low-risk women; 21.5% of women were designated to be at high risk with the addition of BMD. Fifteen percent of all women were reclassified after adding BMD to clinical risk factors, most of whom lay near the intervention threshold. When a high probability of reclassification was accepted (without a BMD test) for high risk to low risk (p1< or = 0.8) and a low probability accepted for low to high risk (P2 < or = 0.2), BMD tests would be required in only 21% of the population.
We conclude that the use of clinical risk factors can identify elderly women at high fracture risk and that such patients have a low average BMD. BMD testing is required, however, in a minority of women--a fraction that depends on the probabilities accepted for classification and the thresholds of risk chosen. These findings need to be validated in other cohorts at different ages and from different regions of the world.
本研究的目的是开发一种方法，以优化骨密度测量在病例发现策略中的作用。我们对从英国谢菲尔德及周边地区随机选取的2113名年龄≥75岁的女性进行了研究。基线评估包括髋部骨密度和临床风险因素。随访6723人年，观察女性的死亡和骨折情况。
采用泊松模型确定有无骨密度时所有骨折和死亡的显著风险因素，并使用风险函数计算骨折概率。根据有无骨密度评估，将女性按骨折概率进行分类。将10年骨折概率阈值35%作为干预阈值。通过逻辑回归分析有无骨密度评估时风险分类（即高于或低于阈值概率）的不一致情况，作为重新分类的概率。年龄、既往骨折史、使用皮质类固醇以及低体重指数被确定为显著的临床风险因素。
基于这些临床风险因素，共有16.8%的女性被归类为高风险。这些患者的平均骨密度比低风险女性低约1个标准差；加上骨密度因素后，21.5%的女性被认定为高风险。在临床风险因素中加入骨密度后，15%的女性被重新分类，其中大多数接近干预阈值。当接受高风险到低风险（p1≤0.8）的高重新分类概率（无骨密度检测）以及低风险到高风险（P2≤0.2）的低重新分类概率时，仅21%的人群需要进行骨密度检测。
我们得出结论，使用临床风险因素可以识别骨折风险高的老年女性，且此类患者的平均骨密度较低。然而，少数女性需要进行骨密度检测——这一比例取决于分类接受的概率和所选的风险阈值。这些发现需要在来自世界不同地区、不同年龄的其他队列中进行验证。
